Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways.

Failure of animal models to predict hepatotoxicity in humans has created a push to develop biological pathway-based alternatives, such as those that use in vitro assays. Public screening programs (e.g., ToxCast/Tox21 programs) have tested thousands of chemicals using in vitro high-throughput screening (HTS) assays. Developing pathway-based models for simple biological pathways, such as endocrine disruption, has proven successful, but development remains a challenge for complex toxicities like hepatotoxicity, due to the many biological events involved. To this goal, we aimed to develop a computational strategy for developing pathway-based models for complex toxicities. Using a database of 2171 chemicals with human hepatotoxicity classifications, we identified 157 out of 1600+ ToxCast/Tox21 HTS assays to be associated with human hepatotoxicity. Then, a computational framework was used to group these assays by biological target or mechanisms into 52 key event (KE) models of hepatotoxicity. KE model output is a KE score summarizing chemical potency against a hepatotoxicity-relevant biological target or mechanism. Grouping hepatotoxic chemicals based on the chemical structure revealed chemical classes with high KE scores plausibly informing their hepatotoxicity mechanisms. Using KE scores and supervised learning to predict in vivo hepatotoxicity, including toxicokinetic information, improved the predictive performance. This new approach can be a universal computational toxicology strategy for various chemical toxicity evaluations.

Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.

Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relationship (QSAR) models. However, conventional QSAR models have limited training data, leading to low predictivity for new compounds. We developed a data-driven modeling approach for constructing carcinogenicity-related models and used these models to identify potential new human carcinogens. To this goal, we used a probe carcinogen dataset from the US Environmental Protection Agency's Integrated Risk Information System (IRIS) to identify relevant PubChem bioassays. Responses of 25 PubChem assays were significantly relevant to carcinogenicity. Eight assays inferred carcinogenicity predictivity and were selected for QSAR model training. Using 5 machine learning algorithms and 3 types of chemical fingerprints, 15 QSAR models were developed for each PubChem assay dataset. These models showed acceptable predictivity during 5-fold cross-validation (average CCR = 0.71). Using our QSAR models, we can correctly predict and rank 342 IRIS compounds' carcinogenic potentials (PPV = 0.72). The models predicted potential new carcinogens, which were validated by a literature search. This study portends an automated technique that can be applied to prioritize potential toxicants using validated QSAR models based on extensive training sets from public data resources.

Integrating structure annotation and machine learning approaches to develop graphene toxicity models.

Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.

Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.

Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.

For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.

High-Throughput Screening Assay Profiling for Large Chemical Databases.

High-throughput screening (HTS) techniques are increasingly being adopted by a variety of fields of toxicology. Notably, large-scale research efforts from government, industrial, and academic laboratories are screening millions of chemicals against a variety of biomolecular targets, producing an enormous amount of publicly available HTS assay data. These HTS assay data provide toxicologists important information on how chemicals interact with different biomolecular targets and provide illustrations of potential toxicity mechanisms. Open public data repositories, such as the National Institutes of Health's PubChem ( http://pubchem.ncbi.nlm.nih.gov ), were established to accept, store, and share HTS data. Through the PubChem website, users can rapidly obtain the PubChem assay results for compounds by using different chemical identifiers (including SMILES, InChIKey, IUPAC names, etc.). However, obtaining these data in a user-friendly format suitable for modeling and other informatics analysis (e.g., gathering PubChem data for hundreds or thousands of chemicals in a modeling friendly format) directly through the PubChem web portal is not feasible. This chapter aims to introduce two approaches to obtain the HTS assay results for large datasets of compounds from the PubChem portal. First, programmatic access via PubChem's PUG-REST web service using the Python programming language will be described. Second, most users, who lack programming skills, can directly obtain PubChem data for a large set of compounds by using the freely available Chemical In vitro-In vivo Profiling (CIIPro) portal ( http://www.ciipro.rutgers.edu ).

Automatic Quantitative Structure-Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening.

Advances in high-throughput screening (HTS) revolutionized the environmental and health sciences data landscape. However, new compounds still need to be experimentally synthesized and tested to obtain HTS data, which will still be costly and time-consuming when a large set of new compounds need to be studied against many tests. Quantitative structure-activity relationship (QSAR) modeling is a standard method to fill data gaps for new compounds. The major challenge for many toxicologists, especially those with limited computational backgrounds, is efficiently developing optimized QSAR models for each assay with missing data for certain test compounds. This chapter aims to introduce a freely available and user-friendly QSAR modeling workflow, which trains and optimizes models using five algorithms without the need for a programming background.

Aggregation and analysis of secondary pharmacology data from investigational new drug submissions at the US Food and Drug Administration.

Secondary pharmacology studies are utilized by the pharmaceutical industry as a cost-efficient tool to identify potential safety liabilities of drugs before entering Phase 1 clinical trials. These studies are recommended by the Food and Drug Administration (FDA) as a part of the Investigational New Drug (IND) application. However, despite the utility of these assays, there is little guidance on which targets should be screened and which format should be used. Here, we evaluated 226 secondary pharmacology profiles obtained from close to 90 unique sponsors. The results indicated that the most tested target in our set was the GABA benzodiazepine receptor (tested 168 times), the most hit target was adenosine 3 (hit 24 times), and the target with the highest hit percentage was the quinone reductase 2 (NQO2) receptor (hit 29% of the time). The overall results were largely consistent with those observed in previous publications. However, this study also identified the need for improvement in the submission process of secondary pharmacology studies by industry, which could enhance their utility for regulatory purpose. FDA-industry collaborative working groups will utilize this data to determine the best methods for regulatory submission of these studies and evaluate the need for a standard target panel.

Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids.

Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be applicable by screening new analgesic opioids. To this end, a large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. There were 114 PubChem bioassays selected to build quantitative structure-activity relationship (QSAR) models based on the testing results across the probe compounds. The compounds tested in each bioassay were used to develop 12 models using the combination of three machine learning approaches and four types of chemical descriptors. The model performance was evaluated by the coefficient of determination (R 2) obtained from 5-fold cross-validation. In total, 49 models developed for 14 bioassays were selected based on the criteria and were identified to be mainly associated with binding affinities to different opioid receptors. The models for these 14 bioassays were further used to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and ß (ERα and ERß) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERß activations. After training, the resultant network successfully inferred critical relationships among ERα/ERß target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERß signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity.

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERß) activity. Three binary fingerprints (Extended Connectivity FingerPrints, Functional Connectivity FingerPrints, and Molecular ACCess System) were used as chemical descriptors in this study. Each descriptor was combined with four machine-learning and two deep- learning (normal and multitask neural networks) approaches to construct models for all 18 ER assays. The resulting model performance was evaluated using the area under the receiver- operating curve (AUC) values obtained from a fivefold cross-validation procedure. The results showed that individual models have AUC values that range from 0.56 to 0.86. External validation was conducted using two additional sets of compounds (n = 592 and n = 966) with established interactions with nuclear ER demonstrated through experimentation. An agonist, antagonist, or binding score was determined for each compound by averaging its predicted probabilities in relevant assay models as an external validation, yielding AUC values ranging from 0.63 to 0.91. The results suggest that multitask neural networks offer advantages when modeling mechanistically related endpoints. Consensus predictions based on the average values of individual models remain the best modeling strategy for computational toxicity evaluations.

Viral markers in nasopharyngeal carcinoma: A systematic review and meta-analysis on the detection of p16INK4a, human papillomavirus (HPV), and Ebstein-Barr virus (EBV).

PURPOSE: This study aimed to conduct a meta-analysis to investigate the distribution of EBV and HPV stratified according to histological NPC type. MATERIALS & METHODS: We performed a meta-analysis to produce pooled prevalence estimates in a random-effects model. We also performed calculations for attributable fractions of viral combinations in NPC, stratified according to histological type. RESULTS: There was a higher prevalence of HPV DNA in WHO Type I (34.4%) versus WHO Type II/III (18.4%). The attributable fractions of WHO Type I NPC was predominantly double negative EBV(-) HPV(-) NPC (56.4%), and EBV(-) HPV(+) NPC (21.5%), in contrast to the predominant infection in WHO Type II/III which was EBV(+) HPV(-) NPC (87.5%). Co-infection of both EBV and HPV was uncommon, and double-negative infection was more common in WHO Type I NPC. CONCLUSION: A significant proportion of WHO Type I NPC was either double-negative EBV(-)HPV(-) or EBV(-)HPV(+).

Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties.

Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities.

Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches.

Food-derived angiotensin I-converting enzyme (ACE) inhibitory peptides could potentially be used as safe supportive therapeutic products for high blood pressure. Theoretical approaches are promising methods with the advantage through exploring the relationships between peptide structures and their bioactivities. In this study, peptides with ACE inhibitory activity were collected and curated. Quantitative structure-activity relationship (QSAR) models were developed by using the combination of various machine learning approaches and chemical descriptors. The resultant models have revealed several structure features accounting for the ACE inhibitions. 14 new dipeptides predicted to lower blood pressure by inhibiting ACE were selected. Molecular docking indicated that these dipeptides formed hydrogen bonds with ACE. Five of these dipeptides were synthesized for experimental testing. The QSAR models developed were proofed to design and propose novel ACE inhibitory peptides. Machine learning algorithms and properly selected chemical descriptors can be promising modeling approaches for rational design of natural functional food components.

Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction.

The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.

The effect of race in head and neck cancer: A meta-analysis controlling for socioeconomic status.

PURPOSE: To investigate the association between race and ethnicity and prognosis in head and neck cancers (HNC), while controlling for socioeconomic status (SES). MATERIALS AND METHODS: Medline, Scopus, EMBASE, and the Cochrane Library were used to identify studies for inclusion, from database inception till March 5th 2019. Studies that analyzed the role of race and ethnicity in overall survival (OS) for malignancies of the head and neck were included in this study. For inclusion, the study needed to report a multivariate analysis controlling for some proxy of SES (for example household income or employment status). Pooled estimates were generated using a random effects model. Subgroup analysis by tumor sub-site, meta-regression, and sensitivity analyses were also performed. RevMan 5.3, Meta Essentials, and OpenMeta[Analyst] were used for statistical analysis. RESULTS: Ten studies from 2004 to 2019 with a total of 108,990 patients were included for analysis in this study. After controlling for SES, tumor stage, and treatment variables, blacks were found to have a poorer survival compared to whites (HR = 1.27, 95%CI: 1.18-1.36, p < 0.00001). Subgroup analysis by sub-site and sensitivity analysis agreed with the primary result. No differences in survival across sub-sites were observed. Meta-regression did not identify any factors associated with the pooled estimate. CONCLUSIONS: In HNC, blacks have poorer OS compared to whites even after controlling for socioeconomic factors.

Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.

Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.